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We announce the release of an advanced version of the Molecular Evolutionary Genetics Analysis (MEGA) software, which currently contains facilities for building sequence alignments, inferring phylogenetic histories, and conducting molecular evolutionary analysis. In version 6.0, MEGA now enables the inference of timetrees, as it implements our RelTime method for estimating divergence times for all branching points in a phylogeny. A new Timetree Wizard in MEGA6 facilitates this timetree inference by providing a graphical user interface (GUI) to specify the phylogeny and calibration constraints step-by-step. This version also contains enhanced algorithms to search for the optimal trees under evolutionary criteria and implements a more advanced memory management that can double the size of sequence data sets to which MEGA can be applied. Both GUI and command-line versions of MEGA6 can be downloaded from www.megasoftware.net free of charge.
(A) Timetree inferred in MEGA6 and shown in the Tree Explorer, where it is displayed with divergence times and their respective 95% confidence intervals. A scale bar for absolute divergence times is shown. (B) An information panel that can be made visible by pressing the icon marked with an " i " . When focused on a tree node (left side), it shows the internal node identifier, and absolute or relative divergence time as appropriate; when focused on a branch (right side), it displays the local clock rate as well as the relative branch length. (C) A timetable exported using the displayed timetree, which shows the ancestor–descendant relationship along with relative node times, relative branch rates, absolute divergence times, and confidence intervals. Users can display internal node identifiers in the Tree Explorer as well as internal node names, which can be provided in the input topology file. On pressing the " Caption " in the Tree Explorer menu bar, MEGA produces the following text to inform the user about the methods, choices, and data used. Caption: The timetree shown was generated using the RelTime method. Divergence times for all branching points in the user-supplied topology were calculated using the Maximum Likelihood method based on the General Time Reversible model. Relative times were optimized and converted to absolute divergence times (shown next to branching points) based on user-supplied calibration constraints. Bars around each node represent 95% confidence intervals which were computed using the method described in Tamura et al. (2013). The estimated log likelihood value of the topology shown is À247671.60. A discrete Gamma distribution was used to model evolutionary rate differences among sites (4 categories, + G, parameter = 38.07). The tree is drawn to scale, with branch lengths measured in the relative number of substitutions per site. The analysis involved 446 nucleotide sequences. All positions with less than 95% site coverage were eliminated. That is, fewer than 5% alignment gaps, missing data, and ambiguous bases were allowed at any position. There were a total of 1,048 positions in the final data set. Evolutionary analyses were conducted in MEGA6 (Tamura et al. 2013).
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Brief Communication
MEGA6: Molecular Evolutionary Genetics Analysis Version 6.0
Koichiro Tamura,
1,2
Glen Stecher,
3
Daniel Peterson,
3
Alan Filipski,
3
and Sudhir Kumar*
,3,4,5
1
Research Center for Genomics and Bioinformatics, Tokyo Metropolitan University, Hachioji, Tokyo, Japan
2
Department of Biological Sciences, Tokyo Metropolitan University, Hachioji, Tokyo, Japan
3
Center for Evolutionary Medicine and Informatics, Biodesign Institute, Arizona State University
4
School of Life Sciences, Arizona State University
5
Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah, Saudi Arabia
*Corresponding author: E-mail: s.kumar@asu.edu.
Associate editor: S. Blair Hedges
Abstract
We announce the release of an advanced version of the Molecular Evolutionary Genetics Analysis (MEGA) software,
which currently contains facilities for building sequence alignments, inferring phylogenetic histories, and conducting
molecular evolutionary analysis. In version 6.0, MEGA now enables the inference of timetrees, as it implements the
RelTime method for estimating divergence times for all branching points in a phylogeny. A new Timetree Wizard in
MEGA6 facilitates this timetree inference by providing a graphical user interface (GUI) to specify the phylogeny and
calibration constraints step-by-step. This version also contains enhanced algorithms to search for the optimal trees under
evolutionary criteria and implements a more advanced memory management that can double the size of sequence data
sets to which MEGA can be applied. Both GUI and command-line versions of MEGA6 can be downloaded from www.
megasoftware.net free of charge.
Key words: software, relaxed clocks, phylogeny.
The Molecular Evolutionary Genetics Analysis (MEGA) soft-
ware is developed for comparative analyses of DNA and pro-
tein sequences that are aimed at inferring the molecular
evolutionary patterns of genes, genomes, and species over
time (Kumar et al. 1994;Tamura et al. 2011). MEGA is cur-
rently distributed in two editions: a graphical user interface
(GUI) edition with visual tools for exploration of data and
analysis results (Tamura et al. 2011) and a command line
edition (MEGA-CC), which is optimized for iterative and
integrated pipeline analyses (Kumar et al. 2012).
In version 6.0, we have now added facilities for building
molecular evolutionary trees scaled to time (timetrees), which
are clearly needed by scientists as an increasing number of
studies are reporting divergence times for species, strains, and
duplicated genes (e.g., Kumar and Hedges 2011;Ward et al.
2013). For this purpose, we have implemented the RelTime
method, which can be used for large numbers of sequences
comprising contemporary data sets, is the fastest method
among its peers, and is shown to perform well in computer
simulations (Tamura et al. 2012). RelTime produces estimates
of relative times of divergence for all branching points (nodes)
in any phylogenetic tree without requiring knowledge of the
distribution of the lineage rate variation and without using
clock calibrations and associated distributions. Relative time
estimates produced by MEGA will be useful for determining
the ordering and spacing of sequence divergence events in
species and gene family trees. The (relative) branch rates pro-
duced by RelTime will also enable users to determine the
statistical distribution of evolutionary rates among lineages
and detect rate differences between species and duplicated
gene clades. In addition, relative times obtained using molec-
ular data can be directly compared with the times from
nonmolecular data (e.g., fossil record) to test independent
biological hypotheses. The RelTime computation in MEGA6
is highly efficient in terms of both performance and memory
required. For a nucleotide alignment of 765 sequences and
2,000 bp (data from Tamura et al. [2011]), MEGA6 required
just 43 min and 1 GB memory (including the calculation steps
mentioned below). Both time and memory requirements
increase linearly with the number of sequences in MEGA6
(fig. 1). Figure 2 shows a timetree produced by MEGA6 and
displayed in the Tree Explorer, which has been upgraded from
previous versions of MEGA to display confidence intervals
and to export relative divergence times and evolutionary
rates for branches, along with absolute divergence times
and confidence intervals (see below). The Tree Explorer also
allows customization of the timetree display in many ways for
producing publication quality images.
Using calibrations to translate relative times to absolute
times: The relative times produced by the RelTime method
can be directly converted into absolute times when a single
known divergence time (calibration point) based on fossil or
other information is available. This facility is incorporated in
MEGA6 where a global time factor (f), which is computed
from the given calibration point, converts all estimates of
relative times (NTs) to absolute times (ATs) where
AT
x
=fNT
x
for the internal node x.Thisapproachis
taken because NTs are already shown to be linearly related
withthetruetime(Tamura et al. 2012). However, researchers
often use multiple calibration points along with information
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on upper and/or lower bounds on one or more calibration
points. In order to consider those constraints when estimat-
ing f, we have extended the RelTime implementation such
that the estimate of fproduces estimates of AT that satisfy the
calibration constraints. In this case, if there are a range of
values for fthat do not violate the calibration constraints,
then the midpoint of that range becomes the estimate of f.
If one or more of the ATs fall outside the calibration con-
straints, then fis set so that their deviation from the con-
straints is minimized. In this case, NTs for the nodes with
estimated ATs are adjusted to satisfy the calibration con-
straints, such that the estimated ATs for the offending
nodes will lie between the minimum and maximum con-
straint times specified by the user. These adjustments to
NTs are followed by re-optimizing all other NTsinthetree
recursively using the standard RelTime algorithm. Figure 2
shows a timetree display with absolute times in the Tree
Explorer, where 95% confidence intervals are shown for
each node time (see below).
Confidence intervals for time estimates:MEGA6alsopro-
vides confidence intervals for relative and absolute divergence
times, which are necessary to assess the uncertainty in the
estimated time and test biological hypotheses. In this formu-
lation, variance contributed by the presence of rate variation
among lineages (V
R,i
) is combined with the estimated variance
of relative node time (V
NT,i
). We compute V
R,i
using the mean
of the coefficient of variation of lineage rates over all internal
nodes (C
R
). It is obtained by first computing the mean ()
and standard deviation () of the node-to-tip distance for
each internal node in the original tree with branch lengths.
Then, C
R
=P[
i
/
i
]
2
/(n-3), where nis the number of se-
quences. For node i,V
R,i
=(NT
i
ˇC
R
)
2
.Thevarianceof
node height (V
H,i
) is estimated by the curvature method ob-
tained during the maximum likelihood estimation of branch
lengths, and thus relative NTs, for each node. Then, the
variance of NT is V(NT
i
)=V
NT,i
+V
R,i
,whichisusedtogen-
erate a 95% confidence interval. The bounds of this interval in
terms of relative time are then multiplied by the factor fto
provide confidence intervals on absolute times when calibra-
tions are provided. It is important to note that this variance
does not incorporate the uncertainty specified in the calibra-
tion times by the user through the specifications of minimum
and maximum bounds, because the statistical distribution of
the calibration uncertainty is rarely known. Therefore, we only
use the range of calibration bounds during the estimation of f
that converts relative times into absolute times, as described
above, but this range does not affect the size of the confi-
dence interval in any other way. In the future, we plan to
enhance the estimation of fwhen users provide statistical
distributions specifying the calibration uncertainty (see also,
Hedges and Kumar 2004).
Timetree Wizard: In practice, the estimation of timetrees
canbecumbersome,asonemustprovideaphylogeny,a
sequence data set, and calibration points with constraints.
To simplify this process, we have programmed a Timetree
Wizard to enable users to provide all of these inputs through
an intuitive step-by-step graphical interface. Figure 3Ashows
aflowchartoftheTimetree Wizard, where the user first pro-
vides a sequence alignment and a tree topology for use in
building a timetree. MEGA6 validates these inputs by map-
ping (sequence) names in the topology to the names in the
alignment data. If the topology contains a subset of sequences
present in the alignment, MEGA automatically subsets the
sequence data. Additional automatic subsetting of data is
provided in the Analysis Preferences Dialog box (see fig. 3E).
In the next step, the user has the option to provide calibration
constraints by using a new Calibration Editor in MEGA6 where
calibration points are specified by 1) point-and-click on indi-
vidualnodesinthetreedisplay(fig. 3B), 2) selecting name-
pairs from dropdown lists such that their most recent
FIG.1. Time (A)andmemory(B) needed for increasingly larger data sets for timetree calculations in MEGA6. Results shown are from an analysis of a
nucleotide sequence alignment of 765 sequences and 2,000 bp. An increasingly larger number of sequences were sampled from this alignment to obtain
the computational time (minutes) and computer memory (Megabytes, Mb). The time taken increases polynomially with the number of sequences
(4 10
05
x
2
+2.64 10
2
x;R
2
= 0.99), where xis the number of sequences. However, a linear regression also fits well (0.048x;R
2
= 0.93). Similarly, the
memory required increases linearly with the number of sequences (1.52x,R
2
= 0.99). All calculations were performed on the same computer with an
Intel Xeon E5-2665 CPU, 128 GB RAM, and running Windows Server 2012 64-bit edition.
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common ancestor on the topology refers to the desired node
(fig. 3C), and/or 3) uploading a text file containing calibration
constraints in a simple format (fig. 3D). If no calibration
constraints are provided, then only relative times and related
statistical measurements will be produced by MEGA6, but
users still have an option to specify them in the Tree
Explorer where the timetree containing relative times is
displayed.
The next step in Timetree Wizard is for the user to select
various analysis options in the Analysis Preferences Dialog,
including the types of substitutions to consider (e.g., nucleo-
tide, codon, or amino acid), evolutionary model describing
the substitution pattern, distribution of substitution rates
among sites (e.g., uniform or gamma-distributed rates and
the presence of invariant sites), options for excluding certain
alignment positions, and stringency for merging evolutionary
clock rates during timetree analysis. These options are avail-
able in a context-dependent manner based on the type of
sequence data being used in the analysis (e.g., nucleotide,
coding vs. non-coding, or proteins). For coding nucleotide
data, the users may subset the data based on the desired
codon positions or ask MEGA to automatically translate
codons into amino acids and conduct analysis at the protein
sequence level. The data subset options also allow for han-
dling of gaps and missing data, where one can choose to use
all the data or exclude positions that contain a few or more
gaps or missing data (e.g., Partial Deletion option). The strin-
gency for merging clock rates option indicates the statistical
significance to use for deciding conditions in which the
ancestor and descendant rates will be the same (rate merg-
ing), which is important to reduce the number of rate pa-
rameters estimated and to avoid statistical over-fitting. Once
these and other options are set, the RelTime computation
begins.
Other enhancements in MEGA: In addition to the new
timetree system in MEGA6, we have made several other
useful enhancements. First, we have added the subtree-prun-
ing-and-regrafting (SPR) algorithm to search for the optimal
tree under the maximum likelihood (ML) and maximum par-
simony (MP) criteria (Swofford 1998;Nei and Kumar 2000). In
FIG.2. (A) Timetree inferred in MEGA6 and shown in the Tree Explorer, where it is displayed with divergence times and their respective 95% confidence
intervals. A scale bar for absolute divergence times is shown. (B) An information panel that can be made visible by pressing the icon marked with an “i”.
When focused on a tree node (left side), it shows the internal node identifier, and absolute or relative divergence time as appropriate; when focused on a
branch (right side), it displays the local clock rate as well as the relative branch length. (C) A timetable exported using the displayed timetree, which
shows the ancestor–descendant relationship along with relative node times, relative branch rates, absolute divergence times, and confidence intervals.
Users can display internal node identifiers in the Tree Explorer as well as internal node names, which can be provided in the input topology file. On
pressing the “Caption” in the Tree Explorer menu bar, MEGA produces the following text to inform the user about the methods, choices, and data used.
Caption:The timetree shown was generated using the RelTime method. Divergence times for all branching points in the user-supplied topology were
calculated using the Maximum Likelihood method based on the General Time Reversible model. Relative times were optimized and converted to absolute
divergence times (shown next to branching points) based on user-supplied calibration constraints. Bars around each node represent 95% confidence
intervals which were computed using the method described in Tamura et al. (2013). The estimated log likelihood value of the topology shown is 247671.60.
A discrete Gamma distribution was used to model evolutionary rate differences among sites (4 categories, +G, parameter = 38.07). The tree is drawn to
scale, with branch lengths measured in the relative number of substitutions per site. The analysis involved 446 nucleotide sequences. All positions with less
than 95% site coverage were eliminated. That is, fewer than 5% alignment gaps, missing data, and ambiguous bases were allowed at any position. There
were a total of 1,048 positions in the final data set. Evolutionary analyses were conducted in MEGA6 (Tamura et al. 2013).
2727
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addition, the tree-bisection-and-regrafting (TBR) algorithm is
now included to search for the MP trees. These algorithms
replace the close-neighbor-interchange (CNI) approach and
allowforamoreexhaustivesearchofthetreespace
(Swofford 1998;Nei and Kumar 2000). These algorithms
were tested on simulated data sets that were analyzed in
Tamura et al. (2011). The final trees produced by SPR heuristic
search were, on average, more optimal than the true tree, a
phenomenon explained by Nei et al. (1998). Therefore,
MEGA6 heuristic searches are expected to perform well in
practical data analysis.
We have also upgraded MEGA source code to increase the
amount of memory that MEGA can address in 64-bit com-
puters, where it can now use up to 4 GB memory, which is
twice its previous limit. The source code upgrade has also
increased the canvas size in Tree Explorer, which can now
render trees with as many as 4,000 taxa. Finally, we have
implemented a usage analytics system to assess options
andanalysesthatarethemostused.Atthetimeof
installation, users have a choice to participate in this effort,
where we wish to generate a better understanding of the
needs of the user community for prioritizing future develop-
ments. For the future, we have already planned the release of
a full 64-bit version of MEGA as well as support for parti-
tioned ML phylogenetic analyses. An outcome of this effort is
a 64-bit command-line version of MEGA6 that supports the
timetree analysis, which can be downloaded from www.mega
software.net/reltime (last accessed October 19, 2013) and
used for very large sequence data sets.
Acknowledgments
We thank Oscar Murillo for extensive help in testing the
RelTime computations. We would also like to thank Sayaka
Miura, Anna Freydenzon, Mike Suleski, and Abediyi Banjoko
for their invaluable feedback. This work was supported from
research grants from National Institutes of Health
(HG002096-12 to S.K. and HG006039-03 to A.F.) and Japan
FIG.3. (A) The flowchart of the Timetree Wizard. When launching the timetree analysis, a user first provides a data file containing a sequence alignment
and another file containing a phylogeny (topology). (B)TheCalibration Editor is invoked when the user needs to specify calibration constraints, which
contains facilities to mark calibrations on top of the user-specified topology. (C) Users may also specify calibrations selecting two sequence names whose
most recent common ancestor (MRCA) points to the node to use for calibration. (D) The user may also upload constraints via formatted text files for
which two types of formats are supported. In one, the calibration time constraints and the names of two taxa whose MRCA is the node to calibrate are
given (panel Cstyle). In the second, a node name in addition to the time constraints is given and this node name matches an internal node label that is
included in the Newick tree file that contains the topology that is used for the timetree analysis. (E)Analysis Preferences Dialog enables the user to select
methods, models, and data subset options.
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Society for the Promotion of Science (JSPS) grants-in-aid for
scientific research to K.T.
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... Seventy-one COI sequences of bat flies were used for phylogenetic analysis, in which 20 and 51 sequences were obtained from this study and Genbank, respectively. We aligned the sequences with Clustal W [42] algorithm using MEGA 6.06 [43]. We used ModelFinder in IQ-Tree v2.20 [44] to find the best-fit model of nucleotide substitution, which was the General Time-Reversible (GTR) model with gamma-shaped (G) distribution across sites and invariable sites (I). ...
... Trees were visualized using FigTree v1.4.4 [46]. We calculated the pairwise p-distances using MEGA 6.06 [43]. Identities of bat fly species with distinct COI sequences (> 2% difference) [47] were cross-checked with the morphological descriptions and illustrations presented in the literature [31][32][33]. ...
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... ncbi.nlm.nih.gov/Blast.cgi). A phylogenetic tree was constructed using MEGA6 (Tamura et al., 2013) under the Neighbor-Joining algorithm with 1000× bootstraps. ...
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